





import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetriever
from transformers.models.realm.tokenization_realm import VOCAB_FILES_NAMES, RealmTokenizer





class UpperCAmelCase_				( snake_case__ ):


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       tempfile.mkdtemp()
       A_          =       5

       # Realm tok
       A_          =       [
           '''[UNK]''',
           '''[CLS]''',
           '''[SEP]''',
           '''[PAD]''',
           '''[MASK]''',
           '''test''',
           '''question''',
           '''this''',
           '''is''',
           '''the''',
           '''first''',
           '''second''',
           '''third''',
           '''fourth''',
           '''fifth''',
           '''record''',
           '''want''',
           '''##want''',
           '''##ed''',
           '''wa''',
           '''un''',
           '''runn''',
           '''##ing''',
           ''',''',
           '''low''',
           '''lowest''',
       ]
       A_          =       os.path.join(self.tmpdirname ,'''realm_tokenizer''' )
       os.makedirs(__snake_case ,exist_ok=__snake_case )
       A_          =       os.path.join(__snake_case ,VOCAB_FILES_NAMES['''vocab_file'''] )
       with open(self.vocab_file ,'''w''' ,encoding='''utf-8''' ) as vocab_writer:
            vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens] ) )

       A_          =       os.path.join(self.tmpdirname ,'''realm_block_records''' )
       os.makedirs(__snake_case ,exist_ok=__snake_case )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       return RealmTokenizer.from_pretrained(os.path.join(self.tmpdirname ,'''realm_tokenizer''' ) )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       shutil.rmtree(self.tmpdirname )


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       RealmConfig(num_block_records=self.num_block_records )
       return config


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       Dataset.from_dict(
           {
               '''id''': ['''0''', '''1'''],
               '''question''': ['''foo''', '''bar'''],
               '''answers''': [['''Foo''', '''Bar'''], ['''Bar''']],
           } )
       return dataset


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       np.array(
           [
               B'''This is the first record''',
               B'''This is the second record''',
               B'''This is the third record''',
               B'''This is the fourth record''',
               B'''This is the fifth record''',
               B'''This is a longer longer longer record''',
           ] ,dtype=__snake_case ,)
       return block_records


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       RealmRetriever(
           block_records=self.get_dummy_block_records() ,tokenizer=self.get_tokenizer() ,)
       return retriever


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_config()
       A_          =       self.get_dummy_retriever()
       A_          =       retriever.tokenizer

       A_          =       np.array([0, 3] ,dtype='''long''' )
       A_          =       tokenizer(['''Test question'''] ).input_ids
       A_          =       tokenizer(
           ['''the fourth'''] ,add_special_tokens=__snake_case ,return_token_type_ids=__snake_case ,return_attention_mask=__snake_case ,).input_ids
       A_          =       config.reader_seq_len

       A_   ,    A_   ,    A_   ,    A_          =       retriever(
           __snake_case ,__snake_case ,answer_ids=__snake_case ,max_length=__snake_case ,return_tensors='''np''' )

       self.assertEqual(len(__snake_case ) ,2 )
       self.assertEqual(len(__snake_case ) ,2 )
       self.assertEqual(len(__snake_case ) ,2 )
       self.assertEqual(concat_inputs.input_ids.shape ,(2, 1_0) )
       self.assertEqual(concat_inputs.attention_mask.shape ,(2, 1_0) )
       self.assertEqual(concat_inputs.token_type_ids.shape ,(2, 1_0) )
       self.assertEqual(concat_inputs.special_tokens_mask.shape ,(2, 1_0) )
       self.assertEqual(
           tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[0] ) ,['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''first''', '''record''', '''[SEP]'''] ,)
       self.assertEqual(
           tokenizer.convert_ids_to_tokens(concat_inputs.input_ids[1] ) ,['''[CLS]''', '''test''', '''question''', '''[SEP]''', '''this''', '''is''', '''the''', '''fourth''', '''record''', '''[SEP]'''] ,)


  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_config()
       A_          =       self.get_dummy_retriever()
       A_          =       retriever.tokenizer

       A_          =       np.array([0, 3, 5] ,dtype='''long''' )
       A_          =       tokenizer(['''Test question'''] ).input_ids
       A_          =       tokenizer(
           ['''the fourth''', '''longer longer'''] ,add_special_tokens=__snake_case ,return_token_type_ids=__snake_case ,return_attention_mask=__snake_case ,).input_ids
       A_          =       config.reader_seq_len

       A_   ,    A_   ,    A_   ,    A_          =       retriever(
           __snake_case ,__snake_case ,answer_ids=__snake_case ,max_length=__snake_case ,return_tensors='''np''' )

       self.assertEqual([False, True, True] ,__snake_case )
       self.assertEqual([[-1, -1, -1], [6, -1, -1], [6, 7, 8]] ,__snake_case )
       self.assertEqual([[-1, -1, -1], [7, -1, -1], [7, 8, 9]] ,__snake_case )




  def 						__UpperCAmelCase     (							self ):



       """simple docstring"""





       A_          =       self.get_dummy_retriever()
       retriever.save_pretrained(os.path.join(self.tmpdirname ,'''realm_block_records''' ) )

       # Test local path
       A_          =       retriever.from_pretrained(os.path.join(self.tmpdirname ,'''realm_block_records''' ) )
       self.assertEqual(retriever.block_records[0] ,B'''This is the first record''' )

       # Test mocked remote path
       with patch('''transformers.models.realm.retrieval_realm.hf_hub_download''' ) as mock_hf_hub_download:
            A_          =       os.path.join(
                os.path.join(self.tmpdirname ,'''realm_block_records''' ) ,_REALM_BLOCK_RECORDS_FILENAME )
            A_          =       RealmRetriever.from_pretrained('''google/realm-cc-news-pretrained-openqa''' )

       self.assertEqual(retriever.block_records[0] ,B'''This is the first record''' )







import argparse

import torch
from safetensors.torch import load_file

from diffusers import StableDiffusionPipeline
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							StableDiffusionPipeline.from_pretrained(UpperCamelCase_				,torch_dtype=torch.floataa							)

     # load LoRA weight from .safetensors
     snake_case											=							load_file(UpperCamelCase_							)

     snake_case											=							[]

     # directly update weight in diffusers model
     for key in state_dict:
          # it is suggested to print out the key, it usually will be something like below
          # "lora_te_text_model_encoder_layers_0_self_attn_k_proj.lora_down.weight"

          # as we have set the alpha beforehand, so just skip
          if ".alpha" in key or key in visited:
               continue

          if "text" in key:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_TEXT_ENCODER + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.text_encoder
          else:
               snake_case											=							key.split('''.'''							)[0].split(LORA_PREFIX_UNET + '''_'''							)[-1].split('''_'''							)
               snake_case											=							pipeline.unet

          # find the target layer
          snake_case											=							layer_infos.pop(0							)
          while len(UpperCamelCase_							) > -1:
               try:
                    snake_case											=							curr_layer.__getattr__(UpperCamelCase_							)
                    if len(UpperCamelCase_							) > 0:
                         snake_case											=							layer_infos.pop(0							)
                    elif len(UpperCamelCase_							) == 0:
                         break
               except Exception:
                    if len(UpperCamelCase_							) > 0:
                         temp_name += "_" + layer_infos.pop(0							)
                    else:
                         snake_case											=							layer_infos.pop(0							)

          snake_case											=							[]
          if "lora_down" in key:
               pair_keys.append(key.replace('''lora_down'''				,'''lora_up'''							)							)
               pair_keys.append(UpperCamelCase_							)
          else:
               pair_keys.append(UpperCamelCase_							)
               pair_keys.append(key.replace('''lora_up'''				,'''lora_down'''							)							)

          # update weight
          if len(state_dict[pair_keys[0]].shape							) == 4:
               snake_case											=							state_dict[pair_keys[0]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].squeeze(3							).squeeze(2							).to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							).unsqueeze(2							).unsqueeze(3							)
          else:
               snake_case											=							state_dict[pair_keys[0]].to(torch.floataa							)
               snake_case											=							state_dict[pair_keys[1]].to(torch.floataa							)
               curr_layer.weight.data += alpha * torch.mm(UpperCamelCase_				,UpperCamelCase_							)

          # update visited list
          for item in pair_keys:
               visited.append(UpperCamelCase_							)

     return pipeline


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Dict      =       argparse.ArgumentParser()

 parser.add_argument(
     "--base_model_path", default=None, type=str, required=True, help="Path to the base model in diffusers format."
 )
 parser.add_argument(
     "--checkpoint_path", default=None, type=str, required=True, help="Path to the checkpoint to convert."
 )
 parser.add_argument("--dump_path", default=None, type=str, required=True, help="Path to the output model.")
 parser.add_argument(
     "--lora_prefix_unet", default="lora_unet", type=str, help="The prefix of UNet weight in safetensors"
 )
 parser.add_argument(
     "--lora_prefix_text_encoder",
     default="lora_te",
     type=str,
     help="The prefix of text encoder weight in safetensors",
 )
 parser.add_argument("--alpha", default=0.75, type=float, help="The merging ratio in W = W0 + alpha * deltaW")
 parser.add_argument(
     "--to_safetensors", action="store_true", help="Whether to store pipeline in safetensors format or not."
 )
 parser.add_argument("--device", type=str, help="Device to use (e.g. cpu, cuda:0, cuda:1, etc.)")

 _SCREAMING_SNAKE_CASE     :					List[Any]      =       parser.parse_args()

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       args.base_model_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.checkpoint_path
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.dump_path
 _SCREAMING_SNAKE_CASE     :					Dict      =       args.lora_prefix_unet
 _SCREAMING_SNAKE_CASE     :					List[str]      =       args.lora_prefix_text_encoder
 _SCREAMING_SNAKE_CASE     :					Optional[Any]      =       args.alpha

 _SCREAMING_SNAKE_CASE     :					Optional[int]      =       convert(base_model_path, checkpoint_path, lora_prefix_unet, lora_prefix_text_encoder, alpha)

 _SCREAMING_SNAKE_CASE     :					Tuple      =       pipe.to(args.device)
 pipe.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)




